Nonlinear control operation of DFIG based WECS with stability analysis
Bibliographic record
Abstract
Among various wind power generation schemes, variable speed wind turbines utilizing doubly fed induction generator (DFIG) have received enormous attention in recent years because of the improved efficiency and wide range of operation. In most cases the stability issues of the machine are overlooked under variable atmospheric condition which may lead the system to vulnerable situation. As the conventional PI controller suffers from the cross-coupling and system nonlinearity on DFIG based wind energy conversion system (WECS), advanced control techniques are required for stable and efficient performance considering the nonlinear system dynamics. Hence, this paper presents a nonlinear controller to stabilize the DFIG connected with the grid through back to back converters. From dynamic equation of DFIG, the reference torque can be calculated by utilizing the optimum rotor speed constrained under maximum power point tracking condition. The state space model for the DFIG expressed in terms of d-q axis flux linkages of stator and rotor side is employed to obtain the required control signals for the converters. A nonlinear controller is designed based on backstepping algorithm while the suitable Lyapunov function is defined to ensure the error functions of the respective state variables are forced to zero that ensures the global stability of the WECS. The uncertainty of combined turbine-generator inertia is also taken into consideration for the proposed nonlinear controller. It is found from the results that the proposed nonlinear controller can maintain stability of the DFIG based WECS under different operating conditions such as wind speed variation and grid voltage disturbances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".